Direct Neighborhood Discriminant Analysis for Face Recognition

Abstract EN

Face recognition is a challenging problem in computer vision and pattern recognition.

Recently, many local geometrical structure-based techiniques are presented to obtain the low-dimensional representation of face images with enhanced discriminatory power.

However, these methods suffer from the small simple size (SSS) problem or the high computation complexity of high-dimensional data.

To overcome these problems, we propose a novel local manifold structure learning method for face recognition, named direct neighborhood discriminant analysis (DNDA), which separates the nearby samples of interclass and preserves the local within-class geometry in two steps, respectively.

In addition, the PCA preprocessing to reduce dimension to a large extent is not needed in DNDA avoiding loss of discriminative information.

Experiments conducted on ORL, Yale, and UMIST face databases show the effectiveness of the proposed method.